housing <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/landdata-states.csv")
head(housing[1:5])
## State region Date Home.Value Structure.Cost
## 1 AK West 2010.25 224952 160599
## 2 AK West 2010.50 225511 160252
## 3 AK West 2009.75 225820 163791
## 4 AK West 2010.00 224994 161787
## 5 AK West 2008.00 234590 155400
## 6 AK West 2008.25 233714 157458
hist(housing$Home.Value)
library(ggplot2)
ggplot(housing, aes(x = Home.Value)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot(Home.Value ~ Date,
data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
data=subset(housing, State == "TX"))
legend(1975, 400000,
c("MA", "TX"), title="State",
col=c("black", "red"),
pch=c(1, 1))
ggplot(subset(housing, State %in% c("MA", "TX")),
aes(x=Date,
y=Home.Value,
color=State))+
geom_point()
hp2001Q1 <- subset(housing, Date == 2001.25)
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = Land.Value)) +
geom_point()
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = log(Land.Value))) +
geom_point()
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
p1 + geom_point(aes(color = Home.Value)) +
geom_line(aes(y = pred.SC))
p1 +
geom_point(aes(color = Home.Value)) +
geom_smooth()
## `geom_smooth()` using method = 'loess'
p1 +
geom_text(aes(label=State), size = 3)
library("ggrepel")
p1 +
geom_point() +
geom_text_repel(aes(label=State), size = 3)
p1 +
geom_point(aes(size = 2),
color="red")
p1 +
geom_point(aes(color=Home.Value, shape = region))
## Warning: Removed 1 rows containing missing values (geom_point).
# EXERCISE 1
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asia
plot1 <- ggplot(dat, aes(x = CPI, y = HDI))
plot1 + geom_point(aes(color = Region, size = HDI.Rank))
args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack",
## ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA,
## inherit.aes = TRUE)
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack",
## ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL,
## breaks = NULL, closed = c("right", "left"), pad = FALSE,
## na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
## NULL
p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
p2 + geom_histogram(stat = "bin", binwidth=4000)
housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))
## State Home.Value
## 1 AK 147385.14
## 2 AL 92545.22
## 3 AR 82076.84
## 4 AZ 140755.59
## 5 CA 282808.08
## 6 CO 158175.99
## 46 VA 155391.44
## 47 VT 132394.60
## 48 WA 178522.58
## 49 WI 108359.45
## 50 WV 77161.71
## 51 WY 122897.25
ggplot(housing.sum, aes(x=State, y=Home.Value)) +
geom_bar(stat="identity")
#EXERCISE 2
library(ggplot2)
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)
## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(method = "lm")
#EXERCISE 2 cont
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_line(stat = "smooth", method = "loess")
#EXERCISE 2 Bonus
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point() +
geom_smooth(span = .4)
## `geom_smooth()` using method = 'loess'
p3 <- ggplot(housing,
aes(x = State,
y = Home.Price.Index)) +
theme(legend.position="top",
axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
alpha = 0.5,
size = 1.5,
position = position_jitter(width = 0.25, height = 0)))
p4 + scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"))
p4 +
scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
p4 +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
p4 +
scale_color_gradient2(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue",
high = "red",
mid = "gray60",
midpoint = 1994)
#EXERCISE 3
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_manual(name = "Region of the world",
values = c("red"))
p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))
(p5 <- p5 + geom_line() +
facet_wrap(~State, ncol = 10))
p5 + theme_linedraw()
p5 + theme_light()
p5 + theme_minimal() +
theme(text = element_text(color = "turquoise"))
theme_new <- theme_bw() +
theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
text=element_text(size = 12, family = "Serif", color = "ivory"),
axis.text.y = element_text(colour = "purple"),
axis.text.x = element_text(colour = "red"),
panel.background = element_rect(fill = "pink"),
strip.background = element_rect(fill = "orange"))
p5 + theme_new
housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
aes(x=Date)) +
geom_line(aes(y=Home.Value), color="red") +
geom_line(aes(y=Land.Value), color="blue")
library(tidyr)
home.land.byyear <- gather(housing.byyear,
value = "value",
key = "type",
Home.Value, Land.Value)
ggplot(home.land.byyear,
aes(x=Date,
y=value,
color=type)) +
geom_line()
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
#building inital plot
pc1 <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
pc1 + geom_point()
# adding in the trendline
(pc2 <- pc1 +
geom_smooth(aes(group = 1),
method = "lm",
formula = y ~ log(x),
se = FALSE,
color = "red")) +
geom_point()
# making adjustments
pc2 +
geom_point(shape = 1, size = 4)
(pc3 <- pc2 + geom_point(shape = 1, size = 2.5, stroke = 1.25))
#labelling hte points on the graph
pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
"Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
"India", "Italy", "China", "South Africa", "Spane",
"Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
"United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
"New Zealand", "Singapore")
(pc4 <- pc3 +
geom_text(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel)))
library("ggrepel")
(pc4 <- pc3 +
geom_text_repel(aes(label = Country),
color = "gray20",
data = subset(dat, Country %in% pointsToLabel),
force = 10))
#labelling the countries, etc.
dat$Region <- factor(dat$Region,
levels = c("EU W. Europe",
"Americas",
"Asia Pacific",
"East EU Cemt Asia",
"MENA",
"SSA"),
labels = c("OECD",
"Americas",
"Asia &\nOceania",
"Central &\nEastern Europe",
"Middle East &\nnorth Africa",
"Sub-Saharan\nAfrica"))
pc4$data <- dat
pc4
# labelling the axes and sizing them and changing the colors for eac country to match as closely to the real graph
# found a pdf online of all colors in Rstudio and picked the ones that looked the best
library(grid)
(pc5 <- pc4 +
scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)",
limits = c(.9, 10.5),
breaks = 1:10) +
scale_y_continuous(name = "Human Development Index, 2011 (1=Best)",
limits = c(0.2, 1.0),
breaks = seq(0.2, 1.0, by = 0.1)) +
scale_color_manual(name = "",
values = c("deepskyblue4",
"deepskyblue3",
"deepskyblue2",
"darkcyan",
"brown3",
"brown4")) +
ggtitle("Corruption and Human development"))
#making final tweaks to aesthetics
library(grid)
(pc6 <- pc5 +
theme_minimal() +
theme(text = element_text(color = "gray20"),
legend.position = c("top"),
legend.direction = "horizontal",
legend.justification = 0.1,
legend.text = element_text(size = 11, color = "gray10"),
axis.text = element_text(face = "italic"),
axis.title.x = element_text(vjust = -1),
axis.title.y = element_text(vjust = 2),
axis.ticks.y = element_blank(),
axis.line = element_line(color = "gray40", size = 0.5),
axis.line.y = element_blank(),
panel.grid.major = element_line(color = "gray50", size = 0.5),
panel.grid.major.x = element_blank()
))
# the last part would be to make the calculation and add it to the graph however I'm not quite sure how to do this part or how to add to graph.